!pip install cartopy
!pip install pycountry
!pip install geopandas
!pip install --upgrade plotly-express

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import cartopy.crs as ccrs
import cartopy.feature as cfeature

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import plotly.express as px

df = px.data.gapminder().query("year==2007")
fig = px.choropleth(df, locations="iso_alpha",
                    color="lifeExp", # lifeExp is a column of gapminder
                    hover_name="country", # column to add to hover information
                    color_continuous_scale=px.colors.sequential.Plasma)
fig.show()

import plotly.express as px
import geopandas as gpd

df = px.data.election()
geo_df = gpd.GeoDataFrame.from_features(
    px.data.election_geojson()["features"]
).merge(df, on="district").set_index("district")

fig = px.choropleth_mapbox(geo_df,
                           geojson=geo_df.geometry,
                           locations=geo_df.index,
                           color="Joly",
                           center={"lat": 45.5517, "lon": -73.7073},
                           mapbox_style="open-street-map",
                           zoom=8.5)
fig.show()

import plotly.graph_objects as go
import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')

fig = go.Figure(data=go.Choropleth(
    locations = df['CODE'],
    z = df['GDP (BILLIONS)'],
    text = df['COUNTRY'],
    colorscale = 'Blues',
    autocolorscale=False,
    reversescale=True,
    marker_line_color='darkgray',
    marker_line_width=0.5,
    colorbar_tickprefix = '$',
    colorbar_title = 'GDP<br>Billions US$',
))

fig.update_layout(
    title_text='2014 Global GDP',
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ),
    annotations = [dict(
        x=0.55,
        y=0.1,
        xref='paper',
        yref='paper',
        text='Source: <a href="https://www.cia.gov/library/publications/the-world-factbook/fields/2195.html">\
            CIA World Factbook</a>',
        showarrow = False
    )]
)

fig.show()

# Importar los módulos necesarios
import pandas as pd
import plotly.express as px
from urllib.request import urlopen
import json

# Cargar los datos geográficos de África desde el archivo GeoJSON
with urlopen('https://raw.githubusercontent.com/codeforamerica/click_that_hood/master/public/data/africa.geojson') as response:
    counties = json.load(response)

# Leer el archivo CSV con datos de población
df_africa = pd.read_csv("world_population.csv", dtype={"Country/Territory": str})

# Crear el mapa coroplético
fig = px.choropleth(df_africa, locations='Country/Territory', color='2022 Population',
                    color_continuous_scale="Viridis",
                    range_color=(0, 1000000),
                    scope="africa",
                    labels={'2022 Population':'Population'}
                   )
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

import plotly.graph_objects as go

fig = go.Figure(go.Scattergeo())
fig.update_geos(projection_type="natural earth")
fig.update_layout(height=300, margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

import plotly.graph_objects as go

fig = go.Figure(go.Scattergeo())
fig.update_geos(projection_type="orthographic")
fig.update_layout(height=300, margin={"r":0,"t":0,"l":0,"b":0})
fig.show()